Biology Practical Skills

The factor you changed - goes in the first column - on the x (horizontal) axis

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A separate table should be used

to show processing

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Plot mean or rate

on the y axis

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Rules for plotting a line graph

Join points with a ruler unless intermediate points can be predicted with confidence. If you plot a line of best fit there should be the same number of points on either side of the line. Do not extrapolate. Start on first point and finish on last

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Purpose of Water Baths

Maintain a constant temperature of all solutions - solutions are placed in a water bath and left to equilibrate

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Equilibration times vary

based upon volumes of solutions and the difference between starting and target temperatures - leaving solutions for a set time may not be long enough

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Water bath and solution temperatures can be monitored by

placing a thermometer into the liquids

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Room temperature water baths could just be

beakers of water - water temperature changes more slowly than air temperature so fluctuates less

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How often do you monitor water bath temperatures and what do you do to maintain the correct temperature?

Regularly - every two minutes. Add hot or cold water to adjust the temperature quickly and stir to ensure the temperature near the test tubes is the same as the temperature near the thermometer

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Why must temperature be controlled?

It affects enzyme action - Ideally temperatures should be kept around the optimum for enzyme action: 35-40*C

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Define: Optimum temperature / pH

The temperature / pH at which an enzyme / reactions work fastest / best

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Buffer solutions

Maintain a constant pH - changes in pH can affect enzyme action

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Controls

Show that the factor being tested is responsible for the observed effect. All factors bar the one being tested are kept the same but the test factor is omitted or replaced with a harmless/inactive alternate e.g. boiled enzymes & placebos

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Repeats

Allow the calculation of a more reliable mean. Allow you to identify anomalies and reduces the effect of anomalies

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Accuracy

Being as close to the theoretical 'correct' value. Accuracy can be increased by using a measuring device with smaller divisions e.g. mm rather than cm

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Increasing measuring time

Increases accuracy: if measuring times = short the error caused by reaction times forms a large proportion of the total time. Recording stopwatch times to more decimal places does not increase accuracy-human reaction time > more timer precision

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Standard deviation

Shows the spread from the mean, takes all data into account - not just the extremes. Reduces the effect of anomalous data. Can be shown as standard deviation bars on a graph or as a +/- sign after a mean

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If standard deviations overlap

There is no significant difference between the data

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A large standard deviation

Indicates results show a lot of variation / the results are not very reliable

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Calculating Means

Add up all the values and divide by how many there are. Mean should have same number of decimal places as the individual results

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Calculating Rate of reaction

1 / time in seconds for a timed single process e.g. time taken for solution to change colour. Distance / time e.g. distance moved by an air bubble in a capillary tube. Volume / time e.g. the amount of oxygen released from OH in 3 minutes

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Calculating percentage

Final value / (start value x100) - usually small / large x100. Useful when there are different numbers of people/test tubes in experimental groups as percentage gives you a relative proportion for each group

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Calculating Percentage change

(Difference between values/ Starting value) x100. Useful when comparing data where start values are different, it shows a proportional change.

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Calculating ratio

Divide one number by the other (usually large/small)

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Calculating Magnification

Image size / actual size

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Calculating dilutions

Calculate how many times smaller the dilution is than the stock solution. Make the required volume the same number of times smaller, and remove this from the stock solution. Make up the remainder of the required volume using water.

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Describing trends

Most data will show a change in gradient, make sure you emphasise the change in gradient so you have described the pattern fully. Use values from the table/graph to show where the change in gradient occurs

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Evaluating data - often using case studies of drug trials

Make sure your answer shows a balanced argument. Is the sample size large enough to be representative/reliable/identify anomalies? Does the trend support the conclusion? Correlation not causation. Is data incomplete? e.g. one gender